code: def run_prediction_examples(mode="single", visualization=False):
      if mode == "single":
          run_single_example(
              len_forecast=40, visualization=visualization
          )
      if mode == "multiple":
          run_multiple_example(
              path_to_file="../../cases/data/nemo/SSH_points_grid.csv",
              path_to_exog_file="../../cases/data/nemo/SSH_nemo_points_grid.csv",
              out_path=None,
              len_forecast=30,
              visualization=visualization,
          )
  
imports: import os
  from copy import deepcopy
  import numpy
  import pandas
  from matplotlib import pyplot
  from sklearn.metrics import mean_squared_error, mean_absolute_error
  from fedot.core.data.data import InputData
  from fedot.core.data.data_split import train_test_data_setup
  from fedot.core.data.multi_modal import MultiModalData
  from fedot.core.pipelines.node import PipelineNode
  from fedot.core.pipelines.pipeline import Pipeline
  from fedot.core.repository.dataset_types import DataTypesEnum
  from fedot.core.repository.tasks import Task, TsForecastingParams, TaskTypesEnum
label_desc: Use image node_id 16 for calling a global function with example usage: run_prediction_examples(mode, visualization) without return types